Adaptive Learning for Successful Flexible Manufacturing Cell Design: A Case Study

  • Dan Braha
  • Oded Maimon
Part of the Applied Optimization book series (APOP, volume 17)


In Chapter 13 we presented a method (the P-learning algorithm) for adaptive learning of successful designs that is based on the use of statistical experimental design and stochastic search algorithm. This chapter involves a real industrial problem of designing a flexible manufacturing system that was solved based on the P-learning algorithm.


Taguchi Method Functional Requirement Flexible Manufacturing System Interarrival Time Part Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Pegden, C. D., Shannon, R. E., and Sadowski, R. P., Introduction to Simulation Using SIMAN, McGraw-Hill, 1990.Google Scholar
  2. 2.
    Ross, P. J., Taguchi Techniques for Quality Engineering, McGraw-Hill, 1988.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Dan Braha
    • 1
  • Oded Maimon
    • 2
  1. 1.Department of Industrial EngineeringBen Gurion UniversityBeer ShevaIsrael
  2. 2.Department of Industrial EngineeringTel-Aviv UniversityTel-AvivIsrael

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